An extended simultaneous algebraic reconstruction technique (E‐SART) for X‐ray dual spectral computed tomography

Zhang, Qikun; Zhao, Xing; Wang, Feng

doi:10.1002/sca.21306

Cited by 18 publications

(21 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3(C)). As comparisons, we also implement the image-based method, empirical dual energy calibration (EDEC) method (Stenner et al, 2007), nonlocal means filter based (NLM) method (Zeng et al, 2016b), weighted least square and nonlocal means based (WLS-NLM) method (Zhang et al, 2017), material decomposition from inconsistent rays (MDIR) (Maaβ et al, 2009b), and E-SART method (Zhao et al, 2015, Hu et al, 2016), covering all the decomposition categories and including both conventional and state-of-the-art methods. The corresponding descriptions and comparisons are summarized in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…In 2016, Hu et al extended the E-ART method into an simultaneous version, i.e. E-SART (Hu et al, 2016). This method is based on the matrix inversion and has a high degree of parallelism.…”

Section: Introductionmentioning

confidence: 99%

“…Hu et al developed the E-ART method into an extended simultaneous algebraic reconstruction version, i.e. E-SART [24]. This method is based on the matrix inversion and has a high degree of parallelism, thus the convergence rate is improved dramatically.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Locally linear constraint based optimization model for material decomposition

Wang

Zhu

2017

Phys. Med. Biol.

View full text Add to dashboard Cite

Dual spectral computed tomography (DSCT) has a superior material distinguishability than the conventional single spectral computed tomography (SSCT). However, the decomposition process is an illposed problem, which is sensitive to noise. Thus, the decomposed image quality is degraded, and the corresponding signal-to-noise ratio (SNR) is much lower than that of directly reconstructed image of SSCT. In this work, we establish a locally linear relationship between the decomposed results of DSCT and SSCT. Based on this constraint, we propose an optimization model for DSCT and develop an iterative method with image guided filtering. To further improve the image quality, we employ a preprocessing method based on the relative total variation (RTV) regularization. Both numerical simulations and real experiments are performed, and the results confirm the effectiveness of our proposed approach.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In 2016, Hu et al extended the E-ART method into an simultaneous version, i.e. E-SART (Hu et al, 2016). This method is based on the matrix inversion and has a high degree of parallelism.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Locally linear constraint based optimization model for material decomposition

Wang

Zhu

2017

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…40 Multi-domain methods often choose the basis material component images as the searchedfor tuning objective. 41,42 On one hand, an approximate model is established to describe the nonlinear relationship between the acquired projection datasets and the searched-for decomposed material images. On the other hand, spectral distribution needs to be estimated in advance, and some prior knowledge is often introduced to improve the ill-posedness and suppress noise.…”

Section: Introductionmentioning

confidence: 99%

“…Several multi‐domain methods are proposed with statistical models and nonlinear optimizations 40 . Multi‐domain methods often choose the basis material component images as the searched‐for tuning objective 41,42 . On one hand, an approximate model is established to describe the nonlinear relationship between the acquired projection datasets and the searched‐for decomposed material images.…”

Section: Introductionmentioning

confidence: 99%

Locally linear transform based three‐dimensional gradient ‐norm minimization for spectral CT reconstruction

Wang

Deng

et al. 2020

Medical Physics

View full text Add to dashboard Cite

Purpose: Spectral computed tomography (CT) is proposed by extending the conventional CT along the energy dimension. One newly implementation is to employ an energy-discriminating photon counting detector (PCD), which can distinguish photon energy and divide a whole x-ray spectrum into several energy bins with appropriate post-processing steps. The state-of-the-art PCD-based spectral CT has superior energy resolution and material distinguishability, and it further has a great potential in both medical and industrial applications. To improve the reconstruction quality and decomposition accuracy, in this work, we propose an optimization-based spectral CT reconstruction method with an innovational sparsity constraint. Methods: We first employ a locally linear transform to the reconstructed channel images, and the structural similarity along the spectral dimension is effectively converted to a one-dimensional (1D) gradient sparsity. Then, combining the prior knowledge of piecewise constant in the spatial domain (e.g., a two-dimensional (2D) gradient sparsity feature), we unify both spectral and spatial dimensions and establish a joint three-dimensional (3D) gradient sparsity. In addition, we use the L 0-norm to measure the proposed sparsity and incorporate it as a smoothness constraint to concretize a general optimization framework. Furthermore, we develop the corresponding iterative algorithm to solve the optimization problem. Results: Both visual results and quantitative indexes of numerical simulations and phantom experiments demonstrate the proposed method outperform the conventional filtered backprojection (FBP), total variation (TV), 2D L 0-norm (L 0), and TV with low rank (TVLR)-based methods. From the image and ROI comparisons, we find the proposed method performs well in noise suppression, detail maintenance, and decomposition accuracy. However, the FBP suffers severe noise, the TV and L 0 are difficult to work consistently among different energy bins, and the TVLR fails to avoid gray value shift. The image quality assessments, such as peak signal-to-noise ratio (PSNR), normal mean absolute deviation (NMAD). and structural similarity (SSIM), also consistently indicate the proposed method can effectively removing noise and keeping fine structures in both channel-wise reconstructions and material decompositions. Conclusions: By employing a locally linear transform, the structural similarity among spectral channel images is converted to a 1D gradient sparsity and the gray value shift is effectively avoided when the difference measurement is minimized. The 3D L 0-norm jointly and uniformly measures the gradient sparsity in both spectral and spatial dimensions. The cooperation of locally linear transform and 3D L 0-norm well reinforces the global sparse features and keeps the correlation along spectral dimension without bringing gray-value distortions. The corresponding constraint optimization model is fast and stably solved by using an alternative direction technique. Both numerical simulations and phantom experi...

show abstract

An iterative reconstruction method based on monochromatic images for dual energy CT

et al. 2021

Self Cite

View full text Add to dashboard Cite

Purpose Dual‐energy computed tomography (DECT) scans objects using two different X‐ray spectra to acquire more information, which is also called dual spectral CT (DSCT) in some articles. Compared to traditional CT, DECT exhibits superior material distinguishability. Therefore, DECT can be widely used in the medical and industrial domains. However, owing to the nonlinearity and ill condition of DECT, studies are underway on DECT reconstruction to obtain high quality images and achieve fast convergence speed. Therefore, in this study, we propose an iterative reconstruction method based on monochromatic images (IRM‐MI) to rapidly obtain high‐quality images in DECT reconstruction. Methods An IRM‐MI is proposed for DECT. The proposed method converts DECT reconstruction problem from the basis material images decomposition to monochromatic images decomposition to significantly improve the convergence speed of DECT reconstruction by changing the coefficient matrix of the original equations to increase the angle of the high‐ and low‐energy projection curves or reduce the condition number of the coefficient matrix. The monochromatic images were then decomposed into basis material images. Furthermore, we conducted numerical experiments to evaluate the performance of the proposed method. Results The decomposition results of the simulated data and real data experiments confirmed the effectiveness of the proposed method. Compared to the extended algebraic reconstruction technique (E‐ART) method, the proposed method exhibited a significant increase in the convergence speed by increasing the angle of polychromatic projection curves or decreasing the condition number of the coefficient matrix, when choosing the appropriate monochromatic images. Therefore, the proposed method is also advantageous in acquiring high quality and rapidly converged images. Conclusions We developed an iterative reconstruction method based on monochromatic images for the material decomposition for DECT. The numerical experiments using the proposed method validated its capability of decomposing the basis material images. Furthermore, the proposed method achieved faster convergence speed compared to the E‐ART method.

show abstract

An extended simultaneous algebraic reconstruction technique (E‐SART) for X‐ray dual spectral computed tomography

Cited by 18 publications

References 41 publications

Locally linear constraint based optimization model for material decomposition

Locally linear constraint based optimization model for material decomposition

Locally linear transform based three‐dimensional gradient ‐norm minimization for spectral CT reconstruction

An iterative reconstruction method based on monochromatic images for dual energy CT

Contact Info

Product

Resources

About